arXiv 16 Jun 2023 · Econometrics · publishedJournal of Econometrics (2025)
arXiv:2306.09806 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes an Anderson-Rubin (AR) test for the presence of peer effects in panel data without the need to specify the network structure. The unrestricted model of our test is a linear panel data model of social interactions with dyad-specific peer effect coefficients for all potential peers. The proposed AR test evaluates if these peer effect coefficients are all zero. As the number of peer effect coefficients increases with the sample size, so does the number of instrumental variables (IVs) employed to test the restrictions under the null, rendering Bekker's many-IV environment. By extending existing many-IV asymptotic results to panel data, we establish the asymptotic validity of the proposed AR test. Our Monte Carlo simulations show the robustness and superior performance of the proposed test compared to some existing tests with misspecified networks. We provide two applications to demonstrate its empirical relevance.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Crudu, Mellace \ Sándor (2021) `Inference in instrumental variable models with heteroskedasticity and many instruments', Econometric Theory 37(2), 281–310 | 1.000 | 6 | 3 | 100% |
| 2 | de Paula, Rasul \ Souza (2024) `Identifying network ties from panel data: Theory and an application to tax competition', The Review of Economic Studies 92(4),… | 1.000 | 6 | 3 | 100% |
| 3 | Bekker (1994) `Alternative approximations to the distributions of instrumental variable estimators', Econometrica 62(3), 657–681 | 1.000 | 5 | 3 | 100% |
| 4 | Anatolyev \ Gospodinov (2011) `Specification testing in models with many instruments', Econometric Theory 27(2), 427–441 | 0.965 | 10 | 5 | 90% |
| 5 | Donald, Imbens \ Newey (2003) `Empirical likelihood estimation and consistent tests with conditional moment restrictions', Journal of Econometrics 117(1), 55–93 | 0.928 | 4 | 3 | 100% |
| 6 | Mikusheva \ Sun (2022) `Inference with many weak instruments', The Review of Economic Studies 89(5), 2663–2686 | 0.874 | 9 | 2 | 100% |
| 7 | Anatolyev \ Sølvsten (2023) `Testing many restrictions under heteroskedasticity', Journal of Econometrics 236(1), 105473 | 0.874 | 5 | 2 | 100% |
| 8 | Anatolyev (2019) `Many instruments and/or regressors: A friendly guide', Journal of Economic Surveys 33(2), 689–726 | 0.811 | 4 | 2 | 100% |
| 9 | Manski (1993) `Identification of endogenous social effects: the reflection problem', The Review of Economic Studies 60(3), 531–542 | 0.811 | 4 | 2 | 100% |
| 10 | Chao, Swanson, Hausman, Newey \ Woutersen (2012) `Asymptotic distribution of JIVE in a heteroskedastic IV regression with many instruments', Econometric Theory 28(1), 42–86 | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 49 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Cluster-Robust Inference for Quadratic Forms | 0.737 | 3 | 2 |